...

Bibliography

by user

on
2

views

Report

Comments

Transcript

Bibliography
University of Pretoria etd – Joubert, J W (2007)
Bibliography
Aarts, E. and Korst, J. (1989). Simulated Annealing and Boltzmann Machines. WileyInterscience Series in Discrete Mathematics. John Wiley & Sons, Chichester.
Acid, S., de Campos, L. M., Fernández-Luna, J. M., Rodrı́guez, S., Rodrı́guez, J. M., and
Salcedo, J. L. (2004). A comparison of learning algorithms for bayesian networks: a case
study on data from an emergency medical service. Artificial Intelligence in Medicine,
30(3):215–232.
Ahn, B.-H. and Shin, J.-Y. (1991). Vehicle-routeing with time windows and time-varying
congestion. The Journal of the Operational Research Society, 42(5):393–400.
Albus, J. S. (1999). The engineering of mind. Information Sciences, 117(1):1–18.
Alfa, A. S., Heragu, S. S., and Chen, M. (1991). A 3-opt ased simulated annealing algorithm
for vehicle routing problems. Computers & Industrial Engineering, 21(1–4):635–639.
Assad, A. A. (1988). Modeling and implementation issues in routing. In Golden, B. L.
and Assad, A. A., editors, Vehicle Routing: Methods and Studies, volume 16 of Studies in
Management Science and Systems, chapter 2, pages 7–45. North-Holland, Amsterdam.
Banister, D. (1995). Transport and urban development. E & FN Spon, London, 1st edition.
Beaulieu, M. and Gamache, M. (2006). An enumeration algorithm for solving the fleet management problem in underground mines. Computers & Operations Research, 33(6):1606–
1624.
Bezdek, J. C. (1974). Numerical taxonomy with fuzzy sets. Journal of Mathematical Biology,
1:57–71.
138
University of Pretoria etd – Joubert, J W (2007)
Birge, J. R. and Louveaux, F. V. (1988). A multicut algorithm for two-stage stochastic linear
programs. European Journal of Operational Research, 34(3):384–392.
Blanton, J. L. and Wainwright, R. L. (1993). Multiple vehicle routing with time and capacity
constraints using genetic algorithms. In Proceedings of the 5th International Conference
on Genetic Algorithms, pages 452–459, San Francisco, CA. Morgan Kaufmann Publishers.
Bodin, L., Golden, B. L., Assad, A., and Ball, M. O. (1983). The state of the art in the routing
and scheduling of vehicles and crews. Computers & Operations Research, 10(1):63–211.
Bolshakova, N. and Azuaje, F. (2003). Cluster validation techniques for genome expression
data. Signal Processing, 83(4):825–833.
Brandão, J. and Mercer, A. (1997). A tabu search algorithm for the multi-trip vehicle routing
and scheduling problem. European Journal of Operational Research, 100(1):180–191.
Bräysy, O. and Gendreau, M. (2001). Tabu search heuristics for the vehicle routing problem
with time windows. Report stf42 a01022, SINTEF Applied Mathematics, Research Council
of Norway.
Brucker, P. (2004). Scheduling algorithms. Springer-Verlag, Berlin, Germany, 4th edition.
Bullnheimer, B., Hartl, R. F., and Strauss, C. (1999). An improved ant system algorithm
for the vehicle routing problem. Annals of Operations Research, 89:319–328.
Burke, L. I. and Ignizio, J. P. (1992). Neural networks and operations research: An overview.
Computers & Operations Research, 19(3–4):179–189.
Butt, S. E. and Ryan, D. M. (1999). An optimal solution procedure for the multiple tour
maximum collection problem using column generation. Computers & Operations Research,
26(4):427–441.
Caianello (1961). Outline of theory of though-processes and thinking machines. Journal of
Theoretical Biology, 1(2):204–235.
Carter, A. E. and Ragsdale, C. T. (2005). A new approach to solving the multiple traveling
salesperson problem using genetic algorithms. European Journal of Operational Research.
Forthcoming.
Chen, X., Wan, W., and Xu, X. (1998). Modeling rolling batch planning as vehicle routing
problem with time windows. Computers & Operations Research, 25(12):1127–1136.
139
University of Pretoria etd – Joubert, J W (2007)
Choi, E. and Tcha, D.-W. (2006). A column generation approach to the heterogeneous fleet
vehicle routing problem. Computers & Operations Research. Forthcoming.
Christofides, N. (1985). Vehicle routing. In Lawler, E., Lenstra, J., Rinnooy Kan, A., and
Shmoys, D., editors, The Traveling Salesman Problem: A Guided Tour of Combinatorial
Optimization, chapter 12, pages 431–448. John Wiley & Sons, UK.
Christofides, N., Mingozzi, A., and Toth, P. (1979). The vehicle routing problem. In
Christofides, N., Mingozzi, A., Toth, P., and Sandi, C., editors, Combinatorial Optimization, chapter 11, pages 315–338. Wiley-Interscience, New York.
Clarke, G. and Wright, J. (1964). Scheduling of vehicles from a central depot to a number
of delivery points. Operations Research, 12:568–581.
CSIR Transportek (2004). The First State of Logistics Survey for South Africa.
Dermuth, H., Beale, M., and Hagan, M. (2005). Neural Network Toolbox User’s Guide. The
Mathworks, Natick, MA.
Desrochers, M., Desrosiers, J., and Solomon, M. (1992). A new optimization algorithm for
the vehicle routing problem with time windows. Operations Research, 40(2):342–354.
Desrosiers, J., Sauvé, M., and Soumis, F. (1988). Lagrangian relaxation methods for solving
the minimum fleet size multiple traveling salesman problem with time windows. Management Science, 34(8):1005–1022.
Dorigo, M. and Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical
Computer Science, 344(2–3):243–278.
Dorigo, M., Di Caro, G., and Gambardelle, L. M. (1999). Ant algorithms for discrete
optimization. Artificial Life, 5(2):137–172.
Dorigo, M. and Gambardella, L. M. (1997a). Ant colonies for the travelling salesman problem.
BioSystems, 43(2):73–81.
Dorigo, M. and Gambardella, L. M. (1997b). Ant colony system: A cooperative learning
approach to the travelling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1):53–66.
Dorigo, M. and Stützle, T. (2002). Ant Colony Optimization. MIT Press, Cambridge.
140
University of Pretoria etd – Joubert, J W (2007)
Dullaert, W., Janssens, G. K., Sörensen, K., and Vernimmen, B. (2001). New heuristics for
the fleet size and mix vehicle routing problem with time windows. In 9th World Conference
on Transport Research, July 22–27, 2001, COEX Convention Center, Seoul.
Filipec, M., Skrlec, D., and Krajcar, S. (1997). Darwin meets computers: new approach to
multiple depot capacitated vehicle routing problem. In Computational Cybernetics and
Simulation, volume 1 of IEEE International Conference on Systems, Man, and Cybernetics, pages 421–426, California. Systems, Man, and Cybernetics Society of the Institute of
Electrical and Electronic Engineers, Inc, IEEE.
Filipec, M., Skrlec, D., and Krajcar, S. (1998). An efficient implementation of genetic algorithms for constrained vehicle routing problem. In Intelligent Systems for Humans in a
Cyberworld, volume 3 of IEEE International Conference on Systems, Man, and Cybernetics, pages 2231–2236, Florida. Systems, Man, and Cybernetics Society of the Institute of
Electrical and Electronic Engineers, Inc, IEEE.
Fisher, M. L. and Jaikumar, R. (1981). A general assignment heuristic for vehicle routing.
Networks, 11:109–124.
Fleischmann, B., Gietz, M., and Gnutzmann, S. (2004). Time-varying travel times in vehicle
routing. Transportation Science, 38(2):160–173.
Gambardella, L. M., Taillard, E., and Agazzi, G. (1999). MACS-VRPTW: A multiple ant
colony system for vehicle routing problems with time windows. In Corne, D., Dorigo, M.,
and Glover, F., editors, New Ideas in Optimization, pages 63–76. McGraw-Hill, London.
Gath, I. and Geva, A. B. (1989). Unsupervised optimal fuzzy clustering. IEEE Transactions
on Pattern Analysis and Machine Intelligence, 11(7):773–781.
Gehring, H. and Homberger, J. (1999). A paralel hybrid evolutionary metaheuristic for the
vehicle routing problem with time windows. In Miettinen, K. and Mäkelä, M. M., editors,
Proceedings of EUROGEN99 — Short Course on Evolutionary Algorithms in Engineering
and Computer Science, volume No. A 2/1999 of Reports of the Department of Mathematical
Information Technology, pages 57–64, Finland.
Gendreau, M. (2003). An introduction to tabu search. In Glover, F. and Kochenberger,
G. A., editors, Handbook of Metaheuristics, International Series in Operations Research &
Management Science, chapter 2, pages 37–54. Kluwer Academic Publishers, Boston.
141
University of Pretoria etd – Joubert, J W (2007)
Gendreau, M., Laporte, G., Musaraganyi, C., and Taillard, É. D. (1999). A tabu search
heuristic for the heterogeneous fleet vehicle routing problem. Computers & Operations
Research, 26(12):1153–1173.
Gendreau, M., Laporte, G., and Potvin, J.-Y. (1998). Metaheuristics for the vehicle routing
problem. Report g-98-52, Les Cahiers du GERAD.
Gendreau, M., Laporte, G., and Potvin, J.-Y. (2002). Metaheuristics for the capacitated
VRP. In Toth, P. and Vigo, D., editors, The Vehicle Routing Problem, SIAM Monographs
on Discrete Mathematics and Applications, chapter 6, pages 129–154. Society for Industrial
and Applied Mathematics (SIAM), Philadelphia.
Ghiani, G., Guerriero, F., Laporte, G., and Musmanno, R. (2003). Real-time vehicle routing:
Solution concepts, algorithms and parallel computing strategies. European Journal of
Operational Research, 151(1):1–11.
Giaglis, G., Minis, I., Tatarakis, A., and Zeimpekis, V. (2004). Minimizing logistics risks
through real-time vehicle routing and mobile technologies. International Journal of Physical Distribution & Logistics Management, 34(9):749–764.
Gillett, B. E. and Miller, L. R. (1974). A heuristic algorithm for the vehicle-dispatch problem.
Operations Research, 22(2):340–349.
Glover, F. (1986). Future paths for integer programming and links to artificial intelligence.
Computers & Operations Research, 13(5):533–549.
Glover, F. (1990). Tabu search — part II. ORSA Journal on Computing, 2(1):4–32.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning.
Addison-Wesley, Reading, Massachusetts.
Goldberg, D. E. and Lingle, R. (1985). Alleles, loci, and the TSP. In Grefenstette, J. J.,
editor, Proceedings of the First International Conference on Genetic Algorithms and their
Applications, pages 154–159, Hillsdale, N.J. Lawrence Erlbaum Associates.
Golden, B., Assad, A., Levy, L., and Gheysens, F. (1984). The fleet size and mix vehicle
routing problem. Computers & Operations Research, 11(1):49–66.
Hamacher, A., Hochstättler, W., and Moll, C. (2000). Tree partitioning under constraints
— clustering for vehicle routing problems. Discrete Applied Mathematics, 99(1):55–69.
142
University of Pretoria etd – Joubert, J W (2007)
Hill, A., Mabert, V., and Montgomery, D. (1988). A decision support system for the
courier vehicle scheduling problem. Omega, International Journal of Management Science, 16(4):333–345.
Hill, A. V. and Benton, W. (1992). Modelling intra-city time-dependent travel speeds for
vehicle scheduling problems. The Journal of the Operational Research Society, 43(4):343–
351.
Hillier, F. S. and Lieberman, G. J. (2005). Introduction to Operations Research. McGrawHill, New York, 8th edition.
Homberger, J. (2003).
Extended Solomon’s VRPTW instances.
World wide web at
http://www.fernuni-hagen.de/WINF/touren/inhalte/probinst.htm.
Homberger, J. and Gehring, H. (1999). Two evolutionary metaheuristics for the vehicle
routing problem with time windows. INFOR, 37(3):297–318.
Hopfield, J. J. and Tank, D. W. (1985). “Neural” computation of decisions in optimization
problems. Biological Cybernetics, 52(3):141–152.
Hwang, H.-S. (2002). An improved model for vehicle routing problem with time constraint
based on genetic algorithm. Computers & Industrial Engineering, 42(2–4):361–369.
Ibaraki, T., Imahori, S., Kubo, M., Masuda, T., Uno, T., and Yagiura, M. (2005). Effective
local search algorithms for routing and scheduling problems with general time window
constraints. Transportation Science, 39(2):206–232.
Ichoua, S., Gendreau, M., and Potvin, J.-Y. (2003). Vehicle dispatching with time-dependent
travel times. European Journal of Operational Research, 144(2):379–396.
Jeroslow, R. (1979). The theory of cutting-planes. In Christofides, N., Mingozzi, A., Toth,
P., and Sandi, C., editors, Combinatorial Optimization, chapter 2, pages 21–72. WileyInterscience, New York.
Joubert, J. W. (2003). An initial solution heuristic for the vehicle routing and scheduling
problem. Master’s thesis, Industrial and Systems Engineering, University of Pretoria,
South Africa.
Joubert, J. W. and Claasen, S. J. (2006). A sequential insertion heuristic for the initial
solution of a constrained vehicle routing problem. ORiON, 22(1):105–116.
143
University of Pretoria etd – Joubert, J W (2007)
Kall, P. and Wallace, S. (1994). Stochastic Programming. John Wiley & Sons, 1st edition.
Kara, I. and Bektas, T. (2005). Integer linear programming formulations of multiple salesman
problems and its variations. European Journal of Operational Research. Forthcoming.
Karanta, I., Mikkola, T., Bounsaythip, C., Jokinen, O., and Savlova, J. (1999). Genetic
algorithms applied to a wood collection problem. In IEEE International Conference on
Systems, Man, and Cybernetics, volume 4, pages 635–639.
Karayannis, N. B. and Venetsanopoulos, A. N. (1993). Artificial Neural Networks: Learning algorithms, performance evaluation, and aplications. Kluwer International Series in
Engineering and Computer Science. Kluwer Academic Publishers, Boston, Massachusetts.
Kim, D.-W., Lee, K. H., and Lee, D. (2003). Fuzzy cluster validation index based on intercluster proximity. Pattern Recognition Letters, 24(15):2561–2574.
Kirkpatrick, S., Gelatt, C., and Vecchi, M. (1983). Optimisation by simulated annealing.
Science, 20:671–680.
Koskosidis, Y. A., Powell, W. B., and Solomon, M. M. (1992). An optimization-based heuristic for vehicle routing and scheduling with soft time windows. Transportation Science,
26(2):69–85.
Kwon, S. (1998). Cluster validity index for fuzzy clustering. Electronic Letters, 34(22):2176–
2177.
Lambert, V., Laporte, G., and Louveaux, F. (1993). Designing collection routes through
bank branches. Computers & Operations Research, 20(7):783–791.
Laporte, G. (1992). The vehicle routing problem: An overview of exact and approximate
algorithms. European Journal of Operational Research, 59(3):345–358.
Laporte, G., Louveaux, F., and Mercure, H. (1989). Models and exact solutions for a class of
stochastic location-routing problems. European Journal of Operational Research, 39(1):71–
78.
Laporte, G., Louveaux, F., and Mercure, H. (1992). The vehicle routing problem with
stochastic travel times. Transportation Science, 26(3):161–170.
Laporte, G. and Louveaux, F. V. (1993). The integer L-shaped method for stochastic integer
programs with complete recourse. Operations Research Letters, 13(3):133–142.
144
University of Pretoria etd – Joubert, J W (2007)
Laporte, G., Mercure, H., and Nobert, Y. (1986). An exact algorithm for the asymmetrical
capacitated vehicle routing problem. Networks, 16(1):33–46.
Laporte, G. and Nobert, Y. (1987). Exact algorithms for the vehicle routing problem. In
Martello, S., Laporte, G., Minoux, M., and Ribeiro, C., editors, Sureys in Combinatorial
Optimization, volume 31 of Annals of Discrete Mathematics, chapter 5, pages 147–184.
Elsevier Science (North-Holland), Amsterdam.
Lee, C.-G., Epelman, M. A., White III, C. C., and Bozer, Y. A. (2006). A shortest path approach to the multiple-vehicle routing problem with split pickups. Transportation Research
Part B, 40(4):265–284.
Leinbach, P. and Stansfield, T. (2002). Living up to expectations. IE Solutions, 34(11):24–30.
Lenstra, J. and Rinnooy Kan, A. (1981). Complexity of vehicle routing and scheduling
problems. Networks, 11:221–227.
Li, C.-L., Vairaktarakis, G., and Lee, C.-Y. (2005). Machine scheduling with deliveries to
multiple customer locations. European Journal of Operational Research, 164(1):39–51.
Lin, S. (1965). Computer solutions of the traveling salesman problem. The Bell System
Technical Journal, 44:2245–2269.
Liu, F.-H. and Shen, S.-Y. (1999a). The fleet size and mix vehicle routing problem with time
windows. Journal of the Operational Research Society, 50(7):721–732.
Liu, F.-H. and Shen, S.-Y. (1999b). A method for Vehicle Routing Problem with Multiple
Vehicle Types and Time Windows. Proceedings of the National Science Council, Republic
of China, ROC(A), 23(4):526–536.
Louis, S. J., Yin, X., and Yuan, Z. Y. (1999). Multiple vehicle routing with time windows using genetic algorithms. In Proceedings of the 1999 Congress on Evolutionary Computation,
volume 3, pages 1804–1808, Washington, D.C. IEEE.
Maeda, O., Nakamura, M., Ombuki, B. M., and Onaga, K. (1999). A genetic algorithm
approach to vehicle routing problem with time deadlines in geographical information systems. In IEEE International Conference on Systems, Man, and Cybernetics, volume 4,
pages 595–600. IEEE.
145
University of Pretoria etd – Joubert, J W (2007)
Maffioli, F. (1979). The complexity of combinatorial optimization algorithms and the challenge of heuristics. In Christofides, N., Mingozzi, A., Toth, P., and Sandi, C., editors,
Combinatorial Optimization, chapter 5, pages 107–129. Wiley-Interscience, New York.
Malandraki, C. and Daskin, M. S. (1992). Time dependent vehicle routing problems: Formulations, properties and heuristic algorithms. Transportation Science, 26(3):185–200.
Malmborg, C. J. (1996). A genetic algorithm for service level based vehicle scheduling.
European Journal of Operational Research, 93(1):121–134.
Maniezzo, V., Gambardella, L. M., and de Luigi, F. (2004). Ant colony optimization. In
Onwubolu, G. C. and Babu, B. V., editors, New Optimization Techniques in Engineering,
chapter 5, pages 101–117. Springer-Verlag, Berlin.
Matsuyama, Y. (1991). Self-organization via competition, cooperation and categorization
applied to extended vehicle routing problems. In IEEE International Joint Conference on
Neural Networks, pages 385–390.
McCulloch, W. S. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous
activity. Bulletin of Mathematical Biophysics, 5:115–133.
Meuleau, N. and Dorigo, M. (2002). Ant colony optimization and stochastic gradient decent.
Artificial Life, 8(2):103–121.
Michalewicz, Z. (1992). Genetic Algorithms + Data Structures = Evolution Programs. Artificial Intelligence. Springer-Verlag, Berlin.
Middendorf, M., Reischle, F., and Schmeck, H. (2002). Multi colony ant algorithms. Journal
of Heuristics, 8(3):305–320.
Mole, R. H. and Jameson, S. R. (1976). A sequential route-bulding algorithm employing a
generalised savings criterion. Operational Research Quarterly, 27(2):503–511.
Müller, B., Reinhardt, J., and Strickland, M. T. (1995). Neural Networks: An Introduction.
Physics of Neural Networks. Springer-Verlag, Berlin, 2nd edition.
Nagy, G. and Salhi, S. (2005). Heuristic algorithms for single and multiple depot vehicle
routing problems with pickups and deliveries. European Journal of Operational Research,
162:126–141.
146
University of Pretoria etd – Joubert, J W (2007)
Nelson, M. D., Nygard, K. E., Griffin, J. H., and Schreve, W. E. (1985). Implementation
techniques for the vehicle routing problem. Computers & Operations Research, 12(3):273–
283.
Nygard, K. E., Greenberg, P., Bolkan, W. E., and Swenson, E. J. (1988). General assignment
methods for the deadline vehicle routing problem. In Golden, B. L. and Assad, A. A., editors, Vehicle Routing: Methods and Studies, volume 16 of Studies in Management Science
and Systems, chapter 6, pages 107–125. North-Holland, Amsterdam.
Nygard, K. E. and Kadaba, N. (1991). Algorithm management using genetic search for
computer-aided vehicle routing. In 24th Annual Hawaii International Conference on Systems Sciences, volume 3, pages 317–326.
Ochi, L. S., Vianna, D. S., Drummond, L. M. A., and Victor, A. O. (1998). A parallel
evolutionary algorithm for the vehicle routing problem with heterogeneous fleet. Future
Generation Computer Systems, 4(5–6):285–292.
OECD (2003). Delivering the Goods: 21st Century Challenges to Urban Goods Transport.
OECD, Paris, France.
Ong, H., Ang, B., Goh, T., and Deng, C. (1997). A vehicle routing and scheduling problem
with time windows and stochastic demand constraints. Asia Pacific Journal of Operational
Research, 14(1):1–17.
Padberg, M. and Rinaldi, G. (1987). Optimization of a 532-city symmetric travelling salesman problem by branch and cut. Operations Research Letters, 6(1):1–7.
Paessens, H. (1988). The savings algorithm for the vehicle routing problem. European
Journal of Operational Research, 34:336–344.
Pal, N. R. and Bezdek, J. C. (1995). On cluster validity for the fuzzy c-means model. IEEE
Transactions on Fuzzy Systems, 3(3):370–379.
Potvin, J.-Y. (1993). The traveling salesman problem: A neural network perspective. ORSA
Journal on Computing, 5(4):328–348.
Potvin, J.-Y. and Smith, K. A. (2003). Artificial neural networks for combinatorial optimization. In Glover, F. and Kochenberger, G. A., editors, The handbook of metaheuristics, International series in Operations Research & Management Science, chapter 15, pages
429–455. Kluwer Academic Publishers, Boston, Massachusetts.
147
University of Pretoria etd – Joubert, J W (2007)
Potvin, J.-Y., Xu, Y., and Benyahia, I. (2006). Vehicle routing and scheduling with dynamic
travel times. Computers & Operations Research, 33(4):1129–1137.
Powell, W. B. (2003). Dynamic models of transportation operations. In de Kok, A. and
Graves, S. C., editors, Supply Chain Management: Design, Coordination and Operation,
volume 11 of Handbooks in Operations Research and Management Science, chapter 13,
pages 677–756. Elsevier, Amsterdam, Netherlands.
Prins, C. (2004). A simple and effective evolutionary algorithm for the vehicle routing
problem. Computers & Operations Research, 31(12):1985–2002.
Rao, A. R. and Srinivas, V. V. (2006). Regionalization of watersheds by fuzzy cluster analysis.
Journal of Hydrology, 318(1):57–79.
Rardin, R. (1998). Optimization in Operations Research. Prentice Hall, Upper Saddle River,
New Jersey.
Reimann, M., Doerner, K., and Hartl, R. F. (2004). D-ants: Savings based ants divide and
conquer the vehicle routing problem. Computers & Operations Research, 31(4):563–591.
Rezaee, M. R., Lelieveldt, B. P. F., and Reiber, J. H. C. (1998). A new cluster validity index
for the fuzzy c-mean. Pattern Recognition Letters, 19(3–4):237–246.
Righini, G. and Salani, M. (2004). Dynamic programming algorithms for the elementary
shortest path problem with resource constraints. Electronic Notes in Discrete Mathematics,
17:247–249.
Robusté, F., Daganzo, C. F., and Souleyrette, R. R. I. (1990). Implementing vehicle routing
models. Transportation Research Part B, 24(4):263–286.
Rochat, Y. and Taillard, É. D. (1995). Probabilistic diversification and intensification in
local search for vehicle routing. Journal of Heuristics, 1(2):147–167.
Rosenblatt, F. (1962). Principles of Neurodynamics. Spartan Books, Washington, D.C.
Russell, S. J. and Norvig, P. (2003). Artificial Intelligence: A Modern Approach. Prentice
Hall Series in Artificial Intelligence. Prentice Hall, Upper Saddle River, New Jersey, 2nd
edition.
Salhi, A., Sari, M., Saidi, D., and Touati, N. A. C. (1992). Adaption of some vehicle fleet
mix heuristics. OMEGA International Journal of Management Science, 20(5–6):653–660.
148
University of Pretoria etd – Joubert, J W (2007)
Salhi, S. and Rand, G. K. (1993). Incorporating vehicle routing into the vehicle fleet composition problem. European Journal of Operational Research, 66(3):313–330.
Schrage, L. (2002). Optimization modeling with LINGO. LINDO Systems Inc, Illinois, 5th
edition.
Skrlec, D., Filipec, M., and Krajcar, S. (1997). A heuristic modification of genetic algorithm used for solving the single depot capacitated vehicle routing problem. In Intelligent
Information Systems ’97, pages 184–188. IEEE.
Solomon, M. (1987). Algorithms for the vehicle routing and scheduling problems with time
windows. Operations Research, 35(2):254–265.
Spence, M. (1998). Western Cape Provincial Transport Policy. In Freeman, P. and Jamet,
C., editors, Urban transport policy — a sustainable development tool, Rotterdam. CODATU, A.A. Balkema.
Starkweather, T., McDaniel, S., Mathias, K., Whitley, D., and Whitley, C. (1991). A comparison of genetic sequencing operators. In Belew, R. K. and Booker, L. B., editors,
Proceedings of the Fourth International Conference of Genetic Algorithms. San Mateo,
Kaufmann.
Taha, H. (2003). Operations research: an introduction. Pearson Education, Inc., Upper
Saddle River, New Jersey, 7th edition.
Taillard, É. D. (1993). Parallel iterative search methods for vehicle routing problems. Networks, 23:661–673.
Taillard, É. D. (1999). A heuristics column generation method for the heterogeneous fleet
VRP. Operations Research – Recherche opérationnelle, 33:1–14.
Taillard, É. D., Badeau, P., Gendreau, M., Guertin, F., and Potvin, J.-Y. (1997). A tabu
search heuristic for the vehicle routing problem with soft time windows. Transportation
Science, 31(2):170–186.
Taillard, É. D., Laporte, G., and Gendreau, M. (1996). Vehicle routing with multiple use of
vehicles. Journal of the Operational Research Society, 47(8):1065–1070.
Tan, K. C., Lee, L. H., and Ou, K. (2001a). Artificial intelligence heuristics in solving vehicle
routing problems with time window constraints. Engineering Applications of Artificial
Intelligence, 14(6):825–837.
149
University of Pretoria etd – Joubert, J W (2007)
Tan, K. C., Lee, L. H., Ou, K., and Lee, L. H. (2001b). A messy genetic algorithm for
the vehicle routing problem with time window constraints. In Congress on Evolutionary
Computation, volume 3, pages 679–686.
Tan, K. C., Lee, L. H., Zhu, Q. L., and Ou, K. (2001c). Heuristic methods for vehicle routing
problem with time windows. Artificial Intelligence in Engineering, 15(3):281–295.
Taniguchi, E., Thompson, R. G., and Yamada, T. (2004). Visions for city logistics. In
Taniguchi, E. and Thompson, R. G., editors, Logistics Systems for Sustainable Cities,
pages 1–16, Oxford, UK. Institute for City Logistics, Elsevier Ltd.
Thangiah, S. R. and Gubbi, A. V. (1993). Effect of genetic sectoring on vehicle routing problems with time windows. In IEEE International Conference on Developing and Managing
Intelligent System Projects, pages 146–153. IEEE.
Thangiah, S. R., Nygard, K. E., and Juell, P. L. (1991). GIDEON: A genetic algorithm
system for vehicle routing with time windows. In Seventh IEEE Conference on Artificial
Intelligence for Applications, volume 1, pages 322–328. IEEE.
Thompson, P. M. and Psaraftis, H. N. (1993). Cyclic transfer algorithms for multivehicle
routing and scheduling problems. Operations Research, 41(5):935–946.
TOP
Program
(2006).
VRP:
A
bibliography.
Retrieved
online
from
http://www.sintef.no/static/am/opti/projects/top/vrp/bibliography.html,
January.
Toth, P. and Vigo, D. (2002a). Models, relaxations and exact approaches for the capacitated
vehicle routing problem. Discrete Applied Mathematics, 123(1–3):487–512.
Toth, P. and Vigo, D. (2002b). An overview of vehicle routing problems. In Toth, P. and Vigo,
D., editors, The Vehicle Routing Problem, SIAM Monographs on Discrete Mathematics
and Applications, chapter 1, pages 1–26. Society for Industrial and Applied Mathematics
(SIAM), Philadelphia.
Toth, P. and Vigo, D. (2003). The granular tabu search and its application to the vehiclerouting problem. INFORMS Journal on Computing, 15(4):333–346.
Tung, D. V. and Pinnoi, A. (2000). Vehicle routing-scheduling for waste collection in hanoi.
European Journal of Operational Research, 125(3):449–468.
150
University of Pretoria etd – Joubert, J W (2007)
Van Breedam, A. (1995). Improvement heuristics for the vehicle routing problem based on
simulated annealing. European Journal of Operational Research, 86(5):480–490.
Van Breedam, A. (2001). Comparing descent heuristics and metaheuristics for the vehicle
routing problem. Computers & Operations Research, 28(4):289–315.
Van Slyke, R. M. and Wets, R. (1969). l-shaped linear programs with applications to optimal
control and stochastic programming. SIAM Journal on Applied Mathematics, 17(4):638–
663.
Vas, P. (1999). Artificial-intelligence-based electrical machines and drives: application of
fuzzy, neural, fuzzy-neural, and genetic-algorithm-based techniques. Oxford University
Press, Oxford.
Winston, W. and Venkataramanan, M. (2003). Introduction to mathematical programming,
volume 1 of Operations Research. Brooks/Cole - Thomson Learning, Pacific Grove, CA,
4th edition.
Xie, X. L. and Beni, G. (1991). A validity measure for fuzzy clustering. IEEE Transactions
on Pattern Analysis and Machine Intelligence, 13(8):841–847.
Xu, Y. and Brereton, R. G. (2005). A comparative study of cluster validation indices applied
to genotyping data. Chemometrics and intelligent laboratory systems, 78(1–2):30–40.
Yamada, T. and Taniguchi, E. (2005). Modelling the effects of urban freight transport
schemes. In Taniguchi, E. and Thompson, R. G., editors, City Logistics, pages 75–89,
Kyoto, Japan. Institute for City Logistics, Institute for City Logistics.
Zhu, K. Q. (2003). A diversity-controlling adaptive genetic algorithm for the vehicle routing
problem with time windows. In IEEE International Conference on Tools with Artificial
Intelligence, pages 176–183. IEEE.
151
Fly UP